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Business Readings
Questions to Consider
Generate Increased Service Profits with MBB  Multidimensional balanced benchmarking can provide more perspectives on cost, quality, and marketing than the conventional technique...as illustrated in this application at a commercial bank. BY H. DAVID SHERMAN, CPA An expanded form of benchmarking, referred to as multidimensional balanced benchmarking (MBB), has identified new paths to improve profitability and operations of several service organizations. Real profit gains and new paths to improved performance have been acknowledged using this approach in three of the 100 largest U.S. banks, the largest mutual fund company, and government service offices. The added benefits of using MBB over traditional benchmarking are analogous to the benefits that have been attributed to activity-based costing (ABC), activity-based management (ABM), balanced scorecards (BSC), and zero customer defection programs. Service organizations have used benchmarking to define good performance in providing services because it is difficult to develop independent efficient standards for services. But reliance on expert judgment by service providers and the inability to test the quality of the service before it is delivered provide challenges that differ from manufacturing. Just as we don't have engineered standards for the ideal way to treat a patient with a chronic condition like arthritis, banks don't have absolute standards for the ideal way to evaluate and process new loans or manage a bank branch. Benchmarking seeks more profitable ways to provide these and other services by seeking and learning from the best practice service providers. If the best practice benchmark is not actually an outstanding performance model, however, efforts to emulate that model can result in suboptimal benefits and may prove costly and dysfunctional in the long run. MBB seeks to identify best practice service providers in organizations more accurately. These more comprehensive best practice service models are used as benchmarks to evaluate, manage, and improve the performance of other service providers within that organization. This case study of a bank branch service network contrasts the benefits of using traditional benchmarking with MBB and illustrates the process of applying MBB. MBB ANALYSIS AND ITS ADVANTAGES MBB balances the analysis of the four key dimensions: marketing, service quality, productivity, and profitability. While benchmarking often considers more than one dimension, MBB ensures that all these dimensions are considered in developing a balanced plan to improve service performance. Moreover, it explicitly requires that the multidimensional nature of each of the four dimensions also be balanced. For example, use of "mystery shoppers" to act as customers and rate service quality captures some but not all key quality dimensions. Customer retention, for example, would not be captured with mystery shopper scores. A bank or retail business that manages its service quality solely by measuring mystery shopper scores may not detect quality weaknesses causing it to lose profitable customers.1 Through this more comprehensive MBB process, service businesses can identify and realize new paths to improved performance by adopting operating methods of best practice offices, branches, managers, and individual service providers such as physicians, lenders, and customer service personnel. TRADITIONAL ANALYSIS VERSUS MBB ANALYSIS OF THREE BRANCHES To compare the use of a traditional versus an MBB analysis, we analyzed the performance of three branches in a network of several hundred branches. These three branches were identified as "comparable" by using a technique referred to as data envelopment analysis to sift through branch data to identify branches with similar operating characteristics in terms of mix of services provided and resources used.2 This selection process is common to most benchmarking analyses, although many different methodologies are used to identify similar branches, some of which simply group branches by deposit size.  Traditional analysis.The traditional analysis would be familiar to most managers of bank branch networks. But because different banks use different methods of analyzing components of branch performance, the traditional analysis will vary from bank to bank. For example, a review of branch profit and loss statements used in 14 banks found that no two banks include the same data. Some banks provide a transfer price to credit revenue to a branch that handles a customer from another branch, and others ignore this matter. Others ignore revenues on deposits and focus on contribution defined as fee revenues less direct expenses. The objectives of these analyses in different service organizations may be identical; however, the specific way performance dimensions are measured varies based on available data, objectives, and management's judgments. The general view that a number of commercial banks have of their branch operations is summarized in Figure 1. These banks recognize the four key dimensions and measure all or most of them in evaluating branch performance. These dimensions impact decisions about resource allocations for branch expansion and contraction, acquisition and sale of branches, and performance rewards for branch personnel. The four performance dimension scores and the operating data for three branches in this bank that were identified as "comparable" are shown in Table 1. This type of benchmark analysis would be completed for each of the several hundred branches in this bank. (The large number of branches involved, which exceeds 1,000 branches for some banks, may explain why the traditional approach uses as little data as possible.) Branch A's operating data indicate that it has about $78 million of deposits, 26,000 teller transactions per month, and $3,400 per month of direct expenses excluding personnel and rent expenses. Branch A employs a total of 16.5 full-time equivalent (FTE) workers, which includes 10 tellers, and platform, manager, and operations personnel. (Platform personnel provide services for new and closed accounts, loan applications, inquiries, certificates of deposit, and special services. The distinction between teller and platform services has diminished as banks have come to realize that a well-run branch requires coordination and shared responsibility for certain transaction types to meet customer needs.) The performance data include a profitability index that measures branch profit converted to a scale of 0 to 100, with 100 being the highest profitability. No branch exceeds 95 on this scale, and very few are in the high 80s. This index suggests that Branch A is relatively profitable, with a rating of 82, and that Branch C is very profitable, with a rating of 89. Service quality is measured based on scores from a mystery shopper. In this program, trained evaluators pose as customers and rate each branch's customer service based on criteria established by bank management. Branch A's score of 85 is just above the bank average of 84, although many branches achieve scores of 100. This bank does not measure marketing effectiveness separately with respect to market potential. The only available indicator was new accounts and loans completed during the month. Other banks use more sensitive market measures to track marketing performance more comprehensively. Productivity is measured by transactions per teller. Branch A's rate of 2,639 transactions per teller was well below both Branch B (7,700 per teller) and Branch C (13,700 per teller). This bank's traditional analysis of the three branches would proceed as follows:  Profitability. Branch management focuses primarily on branch profits because this number directly impacts bank income and earnings per share (EPS). Because it is one of the largest U.S. banks, these earnings are followed closely by the investment community, which can influence the stock price, the value of stock options, and even the likelihood that a bank will become a takeover target. Branches C and A performed best on these dimensions; Branch B might be considered a poor performer. Typically, the manager of Branch B would be least likely to receive a bonus at this profitability level, while managers of Branches A and C might receive bonuses for their high profitability. Service quality.Service quality is high for Branch B and above average for Branch A. Branch C is below average and management normally would encourage the branch to improve its service quality; however, because of Branch C's high profitability, service quality may not be a major concern. (Note that retention of profitable customers is not considered in this service quality measure.) Marketing effectiveness. Marketing effectiveness, which reflects sales of new products and new accounts, is highest for Branch A. While this is a key dimension, management also realizes this measure is not a comprehensive marketing performance measure. Management may ask Branches B and C to account for their slower development of new customers than Branch A achieved. Productivity. Branch A's productivity, as reflected in the ratios of transactions per teller, is noticeably the weakest, and Branch C's is the strongest. This bank's management evaluates productivity independent of the other dimensions. It normally would request that Branches A and B increase their productivity by using fewer personnel to match the transactions per teller of the best practice branch. In this benchmarking analysis, the best practice in terms of productivity is Branch C, which becomes the model for Branches A and B in transaction processing. WHAT TRADITIONAL ANALYSIS SHOWS The traditional analysis produces apparent reasons for some of the performance differences and suggests actions management might take. Branch A has a high deposit base that generates interest revenues and results in high profitability. This high profitability masks higher operating costs and allows Branch A to appear to be a strong contributor to bank profits. Branch C may be designated the best practice of the three branches in terms of productivity. Management could conclude that because Branch A processes fewer than half the teller transactions processed by Branch C, Branch A should have fewer than half the FTEs and should reduce its teller FTEs to two and its total FTEs to about 3.5 (one-half the FTEs in Branch C). The marketing effectiveness of Branch A is superior to C as measured by new accounts, which may suggest that Branch A is utilizing some of the extra FTEs to boost market share. Such a valuable result would justify a somewhat smaller reduction in FTEs. Branch B also would be expected to reduce its personnel because it processes fewer transactions than Branch C. Hence, Branch B may be challenged to eliminate two teller FTEs and four FTEs overall to approach the best practice benchmark of Branch C. If the bank's management were to implement the changes suggested above, the results probably would be described as a very successful initiative. Branches A and B would reduce expenses and increase profits by eliminating several FTEs. If the benchmark branches used to realign other branch operations are not superior performing branches, however, these savings may be temporary and possibly costly in the long run. For example, if these staff reductions move branches to an artificially low staffing level for their service mix and volume, the branches may have to rehire FTEs to restore service quality in the future. The cost to the bank may include the cost of personnel turnover and possible loss of customers during the period the branch was understaffed. One of the benefits of using more comprehensive techniques, such as MBB, is that the benchmark models are more sensitive to the four performance dimensions. Improved benchmarks, for example, will identify cost reductions where excess resources are being used. Eliminating true excess expenses can result in long-term earnings gains without compromising marketing effectiveness and service quality. MULTIDIMENSIONAL BALANCED BENCHMARKING (MBB) ANALYSIS A more balanced benchmarking of branches, using MBB, is illustrated in Figure 2. Here, evaluation of each of the performance dimensions includes more sensitive, multiple measures of performance. This approach recognizes that measuring one item cannot fully reflect a branch's productivity, quality, marketing effectiveness or profitability. For example, to be more comprehensive, a measure of quality also includes retention of profitable customers; a measure of profitability considers return on investment, contribution, as well as a profit and loss analysis; and the measure of marketing goes beyond new product sales to consider growth in market share.  Productivity is measured using data envelopment analysis (DEA), which considers all resources used by a branch to provide its volume and mix of services.3 Resources explicitly include branch facilities, the various types of direct operating costs, and the distributions of FTEs by job type. Services encompass explicit measures of individual services, for example, regular walk-in deposits are segregated from night deposits, which are more time consuming. DEA provides an overall productivity rating. Branch A's productivity rating is 60%, while B and C are rated 100%. This means that Branch C should be able to provide its service volume and mix with about 60% of the resources it currently uses based on comparison with best practice branches in the system. Branches B and C have 100% ratings, which indicate they are using resources efficiently in providing their volume and mix of services compared with other branches in the system. The data and performance measures used for the MBB analysis of these three branches are presented in Table 2. The comparison of performance measures used in the MBB analysis versus the traditional analysis is suggested in Table 3. MANAGEMENT ACTIONS USING MBB The MBB analysis reaches conclusions that are quite different from the traditional analysis. First, this analysis eliminates Branch C as a comparison branch. Even though the branch is highly profitable, it provides substandard service quality, which could generate loss of customers and reduce future profits. A review of closed accounts suggested that the customer retention rate for Branch C was below the bank average. Management would not want other branches to emulate Branch C because the branch does not demonstrate the balanced benchmark performance necessary for a well-run branch model. Rather, Branch C was asked to improve its service quality, which will require it to add personnel and reduce its profitability...actions to prevent customer defections and loss of future profits. The high transaction volume per teller in Branch C was identified as a possible contributor to the low service quality scores. (Notice that Branch C has fewer deposits than Branch B but is more profitable, primarily due to very lean staff levels.)  With Branch C eliminated from this group, the focus now turns to Branches A and B. Branch A is much more profitable because of its high deposit base. Yet Branch A has lower service quality, lower transaction volume per teller, and lower productivity ratings than Branch B. The conclusion of the MBB analysis is that Branch B is performing better than Branch A. This conclusion is contrary to the conclusion of the traditional analysis. In fact, because of Branch A's high profits, some bank managers would not consider Branch A a prime candidate for improvement in profitability. The MBB analysis of Branch A points to specific actions to improve its profitability. Notice the services provided by Branch B versus Branch A (Table 2). Branch B processes about 80% more deposit and bank check teller transactions and 15 to 20% fewer night deposits and bond transactions. Night deposits are processed by tellers and are more time consuming than most other transactions. Branch B also processes more loans and safe deposit visits, but only about one-third the number of new account applications. While Branch B may be characterized as providing more service than Branch A, Branch B does not provide more of each type of service. Branch B processes fewer night deposits and bond transactions and fewer new accounts than Branch A. Branch B, however, provides high-quality service with fewer personnel than Branch A. Branch B uses about 10% fewer platform FTEs, one-third fewer teller FTEs, and one-half the management FTEs compared with Branch A. Direct nonpersonnel expense is also about 30% lower in Branch B. This analysis raised questions about why Branch A had higher personnel levels and expenses. Branch A previously had justified its resource levels based on its level of profitability. The MBB analysis plainly indicates that the resource levels are not justifiable and are not needed to provide high-quality service. Branch A's manager responded that the higher personnel level was needed because the branch services an upscale, high-income clientele. Branch B is also in an upscale market in a different state. Branch B's manager responded that this branch already has additional staff beyond what it describes as "lean" to provide the additional "hand holding" required for this clientele. The bank took two actions based on this analysis:
In summary, MBB generated the following insights:
WAS MBB REALLY NEEDED? Which branch manager should have been promoted, received a bonus, and been used to train other branch managers? If the manager of Branch C was designated a trainer, this individual might have taught managers ways to generate short-term profits by providing lower-quality and lower-cost service. Both Branches B and C might have been asked to reduce personnel levels based on Branch C's transactions per teller.  If management intervened and disqualified Branch C due to inadequate service quality, as they did, then they might have selected Branch A's manager based on Branch A's impressive profitability and above-average service quality. However, Branch A is profitable primarily because it has a large deposit base that is not attributable to the current manager. Branch A's manager would train other managers to provide above-average service quality with high-cost operations and suboptimal profits. Using the MBB analysis, the bank would reward and promote the manager of Branch B and employ this manager as a trainer for other managers. Branch B would be designated a well-run branch model, and Branch A's performance would be assessed as average. MANAGEMENT ACCOUNTING IMPLICATIONS In an industry like financial services, with intense bottom line and stock price sensitivity, a business organization cannot afford to choose the suboptimal Branch A as the best practice model. Branch A's manager may not risk losing customers due to low service quality, as did the manager of Branch C. Branch A's manager, however will not help the bank generate higher returns on investment. Rather, the bank needs to identify the manager of Branch B to lead it to a best practice level. This conclusion suggests that when a management accountant completes a performance or best practice analysis, it is important to consider whether that analysis could suffer from some of the weaknesses of the traditional approach and whether it would benefit from employing more powerful, comprehensive analytic methods such as MBB.  The three branches were part of a
multistate network of about 200 branches that used MBB to increase
annual branch profitability by almost 10%. These paths to improved
performance identified with MBB were not apparent from the
traditional analysis. Examples of profit enhancements generated from
applying MBB to an entire branch network are described below.
Revised view of branch performance and well-run branch benchmarks. MBB identified best practice benchmark branches and branches that could improve performance in all deposit and transaction size categories. This analysis indicated the distribution of best practice branches and branches with potential to improve performance. It differed from management's prior view and generated two sets of actions to boost branch profits. Large branches with high profitability previously were considered the best performance branches. MBB indicated they are not necessarily and not uniformly the best managed in terms of quality, marketing, and productivity. Only 5% of the network were best practice branches with high transactions and high deposits. In contrast, 16% of the network were underperforming large branches. The large, high-profit branches with potential for greater profitability adopted operating methods of the best practice large branches identified with MBB. They increased their profitability by reducing excess resources. MBB identified a few best practice branches in the small deposit/high transactions category and in small deposit/low transaction categories. This analysis indicated that there were well-run small branches operating within the bank that had not previously been recognized. These small best practice branches were studied to develop well-run small branch models for improving other small branches' operations and profits. The changes identified from best practice branches to improve performance included changing operating hours for customer service, leading to reduced personnel costs. Operating expense savings from MBB. The MBB analysis identified network-wide cost savings opportunities that were not previously apparent. If operating expenses for branches within a state or region were similar, they were not considered excessive. The MBB process compared expense levels adjusted for service volume, quality, and branch characteristics and found branches that had noticeably higher operating costs. Further analysis indicated that high nonpersonnel expenses were concentrated in two states and that they were due largely to telephone services. The identification of higher phone expenses helped the bank negotiate a lower-cost arrangement with the telephone company in one state. In the second state, the bank revised the configuration of the phone system to reduce telephone costs. (Fewer and more geographically dispersed branches made rate reduction a less viable solution in this state.) Balancing marketing and branch management objectives. In several cases, the analysis found that the number of management FTEs in branches exceeded the need in terms of the volume and mix of services and resulted in new branch management responsibilities. First, several branches in stable or declining markets did not warrant a full-time branch manager. These branches were restructured to become satellite branches that share a manager. Second, several branches that appeared to have excess management based on transaction volume were operating in growing markets requiring management to help achieve market penetration goals. For these branches, the bank redefined the manager's responsibilities to include branch marketing activities and, in some cases, added an assistant manager. This required selecting branch managers who could succeed in marketing activities. The managers chosen for these assignments would no longer come from high-profit branches but from MBB best practice branches. Can MBB benefit every service organization? MBB and benchmarking, in general, represent a way an organization can learn how to do things better from its own experience. The process helped this bank understand its own strengths and weaknesses and ways to remedy the latter. The manager of one large branch network described the process as follows: "If you have a branch system that's big enough, in effect, you can benchmark yourself. In a relatively large branch sample, there are going to be some performers as good as anyone in the industry and that's the simplifying assumptions you need to make. But it's a fair one." Does MBB conflict with other methodologies such as activity-based costing (ABC) and balanced scorecards (BSC)? In both cases it can complement those efforts. ABC is designed to determine the cost of services with careful allocation of indirect overhead costs based on the cost drivers.4 It indicates a truer picture of how much services actually cost, and the large components of cost can then be evaluated to determine if the value added justifies those costs. MBB suggests which branches provide services with the least resources while maintaining high service quality and marketing effectiveness. These branches serve as the cost model or standard and indicate what the service should cost based on best practices. MBB insights about how much a service should cost combined with the ABC view of how much it does cost can enable the ABC analysis to focus on actual costs in best practice branches and move the organization closer to an optimal cost structure. Balanced scorecards such as were used by Chemical Bank redefine strategies to achieve business objectives.5 Some of these directions require initiatives to improve branch productivity, marketing, and service quality. When these types of branch initiatives are designated as key elements in achieving business goals, MBB could be one of the initiatives employed to define specific changes to branch operations to achieve these goals. MBB, like other multidimensional analytic techniques, applies basic logical analysis more comprehensively and with more depth than more traditional analyses. It is not surprising that analyzing more data in greater depth yields valuable new insights. The question is whether the added efforts and costs justify the added benefits. While the bank case study would suggest the cost is justified, each organization will have different characteristics, size, and complexity. MBB is likely to be more valuable for large complex service organizations where existing approaches are not adequately sensitive in balancing the four key dimensions...profitability, quality, marketing, and productivity...and where data on these four dimensions are available. Applying MBB in other service settings such as HMOs, customer service centers, and government services has provided new insights about ways to improve service performance. While the specific actions will differ for each type of service business, the common results will be actions to improve performance consistent with the objectives of a service business. H. David Sherman, CPA, is associate professor, accounting group, College of Business Administration, Northeastern University, Boston, Mass. He submitted this article through the Boston Chapter. He can be contacted at (617) 373-4640 or e-mail at hsherman@Lynx.neu.edu. 1 F. Reichheld and E. Sasser, "Zero Defections: Quality Comes to Services," Harvard Business Review, Sept.-Oct. 1990, pp. 105-111. 2 H. David Sherman and George Ladino , "Managing Bank Branch Productivity Using Data Envelopment Analysis," Interfaces, Vol. 25, No. 2, March-April 1995, pp. 60-73. 3 Sherman and Ladino. 4 J. Ness and T. Cucuzza, "Tapping the full potential of ABC," Harvard Business Review, July-August 1995, pp. 130-138. 5 Norman Klein and Robert Kaplan, "Chemical Bank: Implementing the Balanced Scorecard," Harvard Business School Case #9-195-210, March 1995.
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